scholarly journals Dihedral Group D4—A New Feature Extraction Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 548
Author(s):  
Puneet Sharma
Keyword(s):  
Dihedral Group ◽  
Feature Vector ◽  
Feature Descriptor ◽  
Image Region ◽  
Histograms Of Oriented Gradients ◽  
Wide Range ◽  
Extraction Algorithm ◽  
Complete Set ◽  
New Feature ◽  
Group D

In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

scholarly journals Target Matching Recognition for Satellite Images Based on the Improved FREAK Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yantong Chen ◽  
Wei Xu ◽  
Yongjie Piao
Keyword(s):  
Satellite Remote Sensing ◽  
Large Data ◽  
Scale Space ◽  
Feature Descriptor ◽  
Test Results ◽  
Feature Detector ◽  
Simulation Test ◽  
Extraction Algorithm ◽  
New Feature ◽  
Target Matching

Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature extractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching recognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key point (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and then transform the feature vector acquired by the FREAK descriptor from decimal into binary. We reduce the quantity of data in the computer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms other relevant methods in terms of robustness and accuracy.

scholarly journals Some characterizatsion of coprime graph of dihedral group D 2n

2021 ◽  
Vol 1722 ◽  
pp. 012051
Author(s):  
A G Syarifudin ◽  
Nurhabibah ◽  
D P Malik ◽  
I G A W Wardhana
Keyword(s):  
Dihedral Group ◽  
Group D

scholarly journals Research on laser stripe characteristics and center extraction algorithm for desktop laser scanner

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Xiaoquan Shi ◽  
Yazhou Sun ◽  
Haitao Liu ◽  
Linqi Bai ◽  
Chonghao Lin
Keyword(s):  
Laser Scanner ◽  
Bright Spot ◽  
Distribution Characteristics ◽  
Ambient Light ◽  
Centroid Method ◽  
Laser Stripe ◽  
Wide Range ◽  
Laser Scanners ◽  
Extraction Algorithm ◽  
Large Width

AbstractThis study presents laser stripe center extraction algorithm for desktop-level 3D laser scanners. The laser stripe center extraction accuracy is an important factor affecting 3D scanning result. Desktop-level devices should have adaptability of a wide range of scanning objects. In this paper, laser stripe energy distribution characteristics with different laser stripe width, ambient light, materials and colors are obtained by experiments. Experiment results show that waveforms of bright spot, low brightness stripe and stripe with large width are complex or easily disturbed, so the center extraction algorithm of them are studied. The extraction effects of extremum method, gradient method and gray centroid method under different conditions are compared. Based on traditional grayscale value, a weighted grayscale value is proposed to extract laser stripe center. Standard deviations of extracted pixel position and fitting pixel position are calculated by different method with different weighted grayscale value. For different conditions, especially for different ambient light intensity, weight matrix plays an important role to extraction result.

scholarly journals Strong duality for metacyclic groups

2002 ◽  
Vol 73 (3) ◽  
pp. 377-392 ◽  
Author(s):  
R. Quackenbush ◽  
C. S. Szabó
Keyword(s):  
Finite Group ◽  
Dihedral Group ◽  
Strong Duality ◽  
Sylow Subgroups ◽  
Metacyclic Groups ◽  
Group D

AbstractDavey and Quackenbush proved a strong duality for each dihedral group Dm with m odd. In this paper we extend this to a strong duality for each finite group with cyclic Sylow subgroups (such groups are known to be metacyclic).

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

scholarly journals A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingchao Li ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Cloud Model ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Communication Signals ◽  
Low Snr ◽  
Feature Extraction Algorithm ◽  
Cloud Characteristics ◽  
Extraction Algorithm ◽  
New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

scholarly journals Feature Recognition for Manufacturability Analysis

1994 ◽  
Author(s):  
William C. Regli ◽  
Satyandra K. Gupta ◽  
Dana S. Nau
Keyword(s):  
Time Complexity ◽  
Material Removal ◽  
Feature Recognition ◽  
Solid Modeling ◽  
Cad Model ◽  
Worst Case ◽  
Machining Features ◽  
Automated Recognition ◽  
Wide Range ◽  
Complete Set

Abstract While automated recognition of features has been attempted for a wide range of applications, no single existing approach possesses the functionality required to perform manufacturability analysis. In this paper, we present a methodology for taking a CAD model of a part and extracting a set of machinable features that contains the complete set of alternative interpretations of the part as collections of MRSEVs (Material Removal Shape Element Volumes, a STEP-based library of machining features). The approach handles a variety of features including those describing holes, pockets, slots, and chamfering and filleting operations. In addition, the approach considers accessibility constraints for these features, has an worst-case algorithmic time complexity quadratic in the number of solid modeling operations, and modifies features recognized to account for available tooling and produce more realistic volumes for manufacturability analysis.

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

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